GROWING HOPE

Understanding Child Growth & HIV Awareness Worldwide

Author

Lukman Khiruddin

Published

April 22, 2025

Introduction

In every corner of our world, the journey of childhood unfolds with both promise and pitfalls. Where the glow of hope is often shadowed by the realities of stunted growth and the silent spread of HIV.

This dashboard invites you to explore the global landscape of child development, weaving together vivid data on child stunting and HIV awareness to reveal the stark contrasts and hidden connections shaping young lives.

Show code

# Import necessary libraries
import pandas as pd
import numpy as np
import plotly.express as px
import plotly.graph_objects as go
from plotly.subplots import make_subplots
import geopandas as gpd
import json
import matplotlib.pyplot as plt

# Define color scheme to match R's ggplot2
COLORS = {
    'green': '#00BA38',
    'red': '#F8766D',
    'blue': '#619CFF',
    'orange': '#FF9E4A',
    'yellow': '#B79F00',
    'background': '#F0F0F0',
    'grid': '#CCCCCC',
    'text': '#000000'
}

HIV Knowledge Among Youth by Country

This comparison highlights the percentage of young people (aged 15–24) who possess comprehensive, correct knowledge of HIV across countries. By displaying both the leaders and laggards, we can better understand the global disparities in HIV education. This helps policymakers identify both success stories to emulate and areas where urgent action is needed.

Show code for top countries chart

# Filter HIV knowledge data
hiv_knowledge_data = combined_indicators[
    combined_indicators['indicator'].str.contains('comprehensive, correct knowledge of HIV', case=False, na=False)
]
hiv_knowledge_data = hiv_knowledge_data[~hiv_knowledge_data['obs_value'].isna()]

# Get the most recent data for each country and sex
recent_hiv_knowledge = hiv_knowledge_data.sort_values('time_period', ascending=False).groupby(['country', 'sex']).first().reset_index()

# Calculate average knowledge by country and sort
avg_knowledge = recent_hiv_knowledge.groupby('country')['obs_value'].mean().reset_index()
avg_knowledge = avg_knowledge.sort_values('obs_value', ascending=True)

# Get top 5 countries
top_5_countries = avg_knowledge.tail(5)

# Create interactive bar chart
fig = go.Figure()

fig.add_trace(go.Bar(
    x=top_5_countries['obs_value'],
    y=top_5_countries['country'],
    orientation='h',
    marker_color='darkgreen',
    text=[f"{x:.1f}%" for x in top_5_countries['obs_value']],
    textposition='outside',
    hovertemplate="<b>%{y}</b><br>Knowledge Rate: %{x:.1f}%<extra></extra>"
))

fig.update_layout(
    title={
        'text': 'Top 5 Countries with Highest HIV Knowledge<br><sup>Youth (15-24 years)</sup>',
        'x': 0.5,
        'xanchor': 'center',
        'font': {'size': 16, 'color': 'black'}
    },
    xaxis_title='Percentage (%)',
    yaxis_title=None,
    xaxis=dict(
        range=[0, 75],
        tickformat='.0f',
        ticksuffix='%'
    ),
    plot_bgcolor='white',
    paper_bgcolor='white',
    showlegend=False,
    height=400,
    margin=dict(t=80, b=50, l=0, r=50)
)

fig.update_xaxes(gridcolor='#cccccc', zeroline=False)
fig.update_yaxes(gridcolor='white')

fig.show()

Show code for bottom countries chart

# Get bottom 5 countries
bottom_5_countries = avg_knowledge.head(5)

# Create interactive bar chart
fig = go.Figure()

fig.add_trace(go.Bar(
    x=bottom_5_countries['obs_value'],
    y=bottom_5_countries['country'],
    orientation='h',
    marker_color='darkgreen',
    text=[f"{x:.1f}%" for x in bottom_5_countries['obs_value']],
    textposition='outside',
    hovertemplate="<b>%{y}</b><br>Knowledge Rate: %{x:.1f}%<extra></extra>"
))

fig.update_layout(
    title={
        'text': 'Bottom 5 Countries with Lowest HIV Knowledge<br><sup>Youth (15-24 years)</sup>',
        'x': 0.5,
        'xanchor': 'center',
        'font': {'size': 16, 'color': 'black'}
    },
    xaxis_title='Percentage (%)',
    yaxis_title=None,
    xaxis=dict(
        range=[0, 75],
        tickformat='.0f',
        ticksuffix='%'
    ),
    plot_bgcolor='white',
    paper_bgcolor='white',
    showlegend=False,
    height=400,
    margin=dict(t=80, b=50, l=0, r=50)
)

fig.update_xaxes(gridcolor='#cccccc', zeroline=False)
fig.update_yaxes(gridcolor='white')

fig.show()

This tabbed comparison reveals stark disparities in HIV knowledge among youth globally. The top-performing countries (primarily in Eastern and Southern Africa) have achieved knowledge rates above 50%, while the lowest-performing countries show rates below 5%.

This significant gap (over 55 percentage points between highest and lowest) highlights implementation disparities in HIV education programs worldwide. Countries at the bottom of the ranking could benefit from adapting successful education strategies from top-performing nations.

Top 4 Countries with HIV Knowledge Trends Over Time

This line chart tracks the evolution of HIV knowledge among males and females in 4 countries over time. The visual uncovers whether both genders are benefiting equally from education efforts, and whether progress is steady, stagnant, or declining. It provides a clear picture of gender gaps and helps guide future interventions to ensure no one is left behind.

Show code for trends chart

# Filter data for comprehensive HIV knowledge
hiv_knowledge_data = combined_indicators[
    combined_indicators['indicator'].str.contains('comprehensive, correct knowledge of HIV', case=False, na=False)
]
hiv_knowledge_data = hiv_knowledge_data[~hiv_knowledge_data['obs_value'].isna()]
hiv_knowledge_data = hiv_knowledge_data[hiv_knowledge_data['sex'].isin(['Female', 'Male'])]

# Count data points by country and sex to find those with the most data
country_data_counts = (
    hiv_knowledge_data.groupby(['country', 'sex'])
    .size()
    .reset_index(name='data_points')
    .groupby('country')
    .agg(
        total_points=('data_points', 'sum'),
        has_both_sexes=('sex', 'nunique')
    )
    .reset_index()
)

# Filter for countries with both sexes and sort by total data points
country_data_counts = country_data_counts[country_data_counts['has_both_sexes'] > 1]
country_data_counts = country_data_counts.sort_values('total_points', ascending=False)

# Get top 4 countries with most data points
top_trend_countries = country_data_counts.head(4)['country'].tolist()

# Filter data for top countries
trend_data = hiv_knowledge_data[hiv_knowledge_data['country'].isin(top_trend_countries)]

# Create subplots
fig = make_subplots(
    rows=2, cols=2,
    subplot_titles=top_trend_countries,
    vertical_spacing=0.15,
    horizontal_spacing=0.1
)

# Plot each country
colors = {'Female': '#F8766D', 'Male': '#619CFF'}  # Match R's default colors
for i, country in enumerate(top_trend_countries):
    row = i // 2 + 1
    col = i % 2 + 1
    
    country_data = trend_data[trend_data['country'] == country]
    
    for sex in ['Female', 'Male']:
        sex_data = country_data[country_data['sex'] == sex]
        
        fig.add_trace(
            go.Scatter(
                x=sex_data['time_period'],
                y=sex_data['obs_value'],
                name=sex,
                line=dict(color=colors[sex], width=2),
                mode='lines+markers',
                marker=dict(size=8),
                showlegend=(i == 0),
                hovertemplate=f"{sex}: %{{y:.1f}}%<extra></extra>"
            ),
            row=row, col=col
        )

fig.update_layout(
    title={
        'text': 'HIV Knowledge Trends by Sex & Country<br><sup>Percentage of youth (15-24) with comprehensive knowledge</sup>',
        'x': 0.5,
        'xanchor': 'center',
        'font': {'size': 16, 'color': 'black'}
    },
    height=600,
    showlegend=True,
    legend=dict(
        orientation="h",
        yanchor="bottom",
        y=-0.2,
        xanchor="center",
        x=0.5
    ),
    plot_bgcolor='white',
    paper_bgcolor='white'
)

# Update all axes
for i in range(1, 5):
    fig.update_yaxes(
        title_text='%',
        range=[0, 60],
        tickformat='.0f',
        ticksuffix='%',
        gridcolor='#cccccc',
        zeroline=False,
        row=(i-1)//2 + 1,
        col=(i-1)%2 + 1
    )
    fig.update_xaxes(
        title_text='Year',
        gridcolor='#cccccc',
        zeroline=False,
        row=(i-1)//2 + 1,
        col=(i-1)%2 + 1
    )

fig.show()

This line chart reveals the evolution of HIV knowledge among young people across countries with the richest trend data, showing patterns of change over time for both males and females.

Global Gender Gap in HIV Knowledge

This scatterplot displays HIV knowledge levels for young people (aged 15-24) across all countries, comparing males and females. Each point represents a country-gender combination, with the most recent available data for each country. The visualization reveals a clear pattern of gender disparity in HIV knowledge globally.

Show code for global comparison

# Get the most recent data for each country and sex combination
hiv_knowledge_global = (
    hiv_knowledge_data
    .sort_values('time_period', ascending=False)
    .groupby(['country', 'sex'])
    .first()
    .reset_index()
)

# Filter to just Male and Female data
hiv_knowledge_global = hiv_knowledge_global[hiv_knowledge_global['sex'].isin(['Female', 'Male'])]

# Create scatter plot with box plots
fig = go.Figure()

# Add box plots
for sex, color in zip(['Female', 'Male'], [COLORS['red'], COLORS['blue']]):
    sex_data = hiv_knowledge_global[hiv_knowledge_global['sex'] == sex]
    
    # Add box plot
    fig.add_trace(go.Box(
        x=[sex] * len(sex_data),
        y=sex_data['obs_value'],
        name=sex,
        marker_color=color,
        boxpoints='all',
        jitter=0.3,
        pointpos=-1.8,
        marker=dict(
            color=color,
            size=8,
            opacity=0.7
        ),
        line=dict(color='black'),
        fillcolor='white',
        hovertemplate="Country: %{text}<br>Value: %{y:.1f}%<extra></extra>",
        text=sex_data['country']
    ))

# Add horizontal line at median
median_value = hiv_knowledge_global['obs_value'].median()
fig.add_shape(
    type='line',
    x0='Female',
    x1='Male',
    y0=median_value,
    y1=median_value,
    line=dict(
        color='darkgray',
        width=2,
        dash='dash'
    )
)

# Update layout
fig.update_layout(
    title={
        'text': 'Global Gender Gap in HIV Knowledge<br><sup>Most recent data on youth (15-24) with comprehensive HIV knowledge by country</sup>',
        'x': 0.5,
        'xanchor': 'center'
    },
    yaxis_title='HIV Knowledge (%)',
    xaxis_title=None,
    yaxis=dict(range=[0, 70]),
    plot_bgcolor='white',
    height=600,
    showlegend=False,
)

fig.update_yaxes(gridcolor=COLORS['grid'], zeroline=False)

fig.show()

For comparison, here is the static version of the same visualization:

Show code for static comparison

# Create country comparison data
country_comparison = (
    hiv_knowledge_global
    .pivot(index='country', columns='sex', values='obs_value')
    .reset_index()
    .dropna()
)

# Create figure
fig = go.Figure()

# Add identity line (y = x)
fig.add_trace(go.Scatter(
    x=[0, 70],
    y=[0, 70],
    mode='lines',
    line=dict(color='gray', dash='dash'),
    showlegend=False,
    hoverinfo='skip'
))

# Add scatter points
fig.add_trace(go.Scatter(
    x=country_comparison['Female'],
    y=country_comparison['Male'],
    mode='markers',
    marker=dict(
        color='#3498db',
        size=10,
        opacity=0.7
    ),
    text=country_comparison['country'],
    hovertemplate='<b>%{text}</b><br>' +
                  'Female Knowledge: %{x:.1f}%<br>' +
                  'Male Knowledge: %{y:.1f}%<extra></extra>',
    showlegend=False
))

# Add trend line with confidence interval
x = country_comparison['Female'].values
y = country_comparison['Male'].values
z = np.polyfit(x, y, 1)
p = np.poly1d(z)

x_smooth = np.linspace(min(x), max(x), 100)
y_smooth = p(x_smooth)

# Calculate confidence intervals
residuals = y - p(x)
std_dev = np.std(residuals)
y_upper = y_smooth + 1.96 * std_dev
y_lower = y_smooth - 1.96 * std_dev

# Add confidence interval
fig.add_trace(go.Scatter(
    x=x_smooth,
    y=y_upper,
    mode='lines',
    line=dict(width=0),
    showlegend=False,
    hoverinfo='skip'
))

fig.add_trace(go.Scatter(
    x=x_smooth,
    y=y_lower,
    mode='lines',
    fill='tonexty',
    fillcolor='rgba(231, 76, 60, 0.2)',
    line=dict(width=0),
    showlegend=False,
    hoverinfo='skip'
))

# Add trend line
fig.add_trace(go.Scatter(
    x=x_smooth,
    y=y_smooth,
    mode='lines',
    line=dict(color='#e74c3c', width=2),
    showlegend=False,
    hoverinfo='skip'
))

# Add annotations for selected points
mask = ((abs(country_comparison['Male'] - country_comparison['Female']) > 10) |
        (country_comparison['Female'] > 45) |
        (country_comparison['Male'] > 45))

for _, row in country_comparison[mask].iterrows():
    fig.add_annotation(
        x=row['Female'],
        y=row['Male'],
        text=row['country'],
        showarrow=False,
        font=dict(size=10),
        yshift=5
    )

# Update layout
fig.update_layout(
    title={
        'text': 'HIV Knowledge: Male vs. Female Comparison<br><sup>Each point represents a country\'s most recent data</sup>',
        'x': 0.5,
        'xanchor': 'center'
    },
    xaxis_title='Female Knowledge (%)',
    yaxis_title='Male Knowledge (%)',
    xaxis=dict(range=[0, 70], dtick=10),
    yaxis=dict(range=[0, 70], dtick=10),
    plot_bgcolor='white',
    height=600,
    showlegend=False,
    annotations=[
        dict(
            text='Points above dashed line: Male knowledge exceeds female knowledge',
            xref='paper',
            yref='paper',
            x=0.5,
            y=-0.15,
            showarrow=False,
            font=dict(size=10)
        )
    ]
)

fig.update_xaxes(gridcolor=COLORS['grid'], zeroline=False)
fig.update_yaxes(gridcolor=COLORS['grid'], zeroline=False)

fig.show()

These visualizations reveal several important patterns in global HIV knowledge:

Persistent Gender Gap: The boxplots show that, on average, males tend to have higher levels of HIV knowledge than females across countries. The median knowledge level for males is noticeably higher than for females.
Wide Variation Between Countries: There is substantial variation in HIV knowledge levels across countries, with some countries showing knowledge rates above 50% while others fall below 5%.
Country-Level Gender Disparities: The comparison plot clearly shows that in most countries (points falling above the dashed line), males have higher knowledge levels than females. However, there are some exceptions where female knowledge exceeds male knowledge.
Positive Correlation: There is a strong positive correlation between male and female knowledge levels within countries. Countries that do well in educating one gender about HIV tend to also do well with the other gender.
Regional Patterns: Some of the highest-performing countries (labeled in the upper right) are clustered in Sub-Saharan Africa, where HIV prevalence is higher and education efforts have been more intensive.

These findings highlight the need for targeted educational interventions that address gender-specific barriers to HIV knowledge, with particular attention to countries and regions where overall knowledge levels remain low.

Child Stunting Burden by Country

This map displays the percentage of children under 5 affected by stunting (low height-for-age) in Africa and Asia, the two continents with the highest burden of child malnutrition. By focusing on these regions, we can better visualize where interventions are most urgently needed and identify patterns of child nutrition challenges.

Show code for Africa map

# Load world map data for Africa
world = gpd.read_file('https://raw.githubusercontent.com/nvkelso/natural-earth-vector/master/geojson/ne_110m_admin_0_countries.geojson')
africa = world[world['CONTINENT'] == 'Africa'].copy()

# Filter stunting data - use only Total values for each country
stunting_data = combined_indicators[
    (combined_indicators['indicator'].str.contains('height.for.age', case=False, na=False) | 
     combined_indicators['indicator'].str.contains('stunting', case=False, na=False)) &
    (~combined_indicators['obs_value'].isna()) &
    (combined_indicators['sex'] == 'Total')  # Only use Total values
]

# Get most recent stunting data for each country
recent_stunting = stunting_data.sort_values('time_period', ascending=False).groupby('country').first().reset_index()

# Ensure values are in proper percentage range (0-100)
recent_stunting['obs_value'] = recent_stunting['obs_value'].apply(lambda x: min(100, max(0, x)))

# Create a country name mapping dictionary
country_mapping = {
    "Congo, Democratic Republic of the": "Democratic Republic of the Congo",
    "Congo, the Democratic Republic of the": "Democratic Republic of the Congo",
    "Congo": "Republic of the Congo",
    "Tanzania, United Republic of": "United Republic of Tanzania",
    "Swaziland": "eSwatini",
    "Côte d'Ivoire": "Ivory Coast",
    "Ivory Coast": "Ivory Coast"
}

# Apply country name mapping
recent_stunting['map_country'] = recent_stunting['country'].apply(
    lambda x: country_mapping.get(x, x)
)

# Create choropleth map
fig = px.choropleth(
    recent_stunting,
    locations='alpha_3_code',
    color='obs_value',
    hover_name='country',
    color_continuous_scale=['lightyellow', 'yellowgreen', 'darkgreen'],
    range_color=[0, 50],  # Set max to 50% as this is a more realistic range for stunting
    labels={'obs_value': 'Stunting Rate (%)'},
    custom_data=['obs_value']
)

# Update hover template to show percentage
fig.update_traces(
    hovertemplate="<b>%{hovertext}</b><br>Stunting Rate: %{customdata[0]:.1f}%<extra></extra>"
)

# Update layout
fig.update_layout(
    title={
        'text': 'Child Stunting Burden in Africa<br><sup>Percentage of children under 5</sup>',
        'x': 0.5,
        'xanchor': 'center',
        'font': {'size': 16, 'color': 'black'}
    },
    geo=dict(
        showframe=False,
        showcoastlines=True,
        projection_type='equirectangular',
        scope='africa',
        bgcolor='white'
    ),
    width=800,
    height=600,
    margin=dict(t=50, b=0, l=0, r=0),
    paper_bgcolor='white',
    plot_bgcolor='white'
)

# Update colorbar
fig.update_coloraxes(
    colorbar_title_text="Stunting Rate (%)",
    colorbar_title_side="right",
    colorbar_thickness=15,
    colorbar_len=0.5,
    colorbar_title_font={'size': 12}
)

fig.show()

Show code for Asia map

# Load world map data for Asia
asia = world[world['CONTINENT'] == 'Asia'].copy()

# Create a country name mapping dictionary for Asia
asia_country_mapping = {
    "Iran, Islamic Republic of": "Iran",
    "Korea, Democratic People's Republic of": "North Korea",
    "Korea, Republic of": "South Korea",
    "Lao People's Democratic Republic": "Laos",
    "Syrian Arab Republic": "Syria",
    "Viet Nam": "Vietnam",
    "Myanmar": "Burma"
}

# Apply country name mapping
recent_stunting = stunting_data.sort_values('time_period', ascending=False).groupby('country').first().reset_index()
recent_stunting['map_country'] = recent_stunting['country'].apply(
    lambda x: asia_country_mapping.get(x, x)
)

# Create choropleth map
fig = px.choropleth(
    recent_stunting,
    locations='alpha_3_code',
    color='obs_value',
    hover_name='country',
    color_continuous_scale=['lightyellow', 'yellowgreen', 'darkgreen'],
    range_color=[0, 50],  # Set max to 50% as this is a more realistic range for stunting
    labels={'obs_value': 'Stunting Rate (%)'},
    custom_data=['obs_value']
)

# Update hover template to show percentage
fig.update_traces(
    hovertemplate="<b>%{hovertext}</b><br>Stunting Rate: %{customdata[0]:.1f}%<extra></extra>"
)

# Update layout
fig.update_layout(
    title={
        'text': 'Child Stunting Burden in Asia<br><sup>Percentage of children under 5</sup>',
        'x': 0.5,
        'xanchor': 'center',
        'font': {'size': 16, 'color': 'black'}
    },
    geo=dict(
        showframe=False,
        showcoastlines=True,
        projection_type='equirectangular',
        scope='asia',
        bgcolor='white'
    ),
    width=800,
    height=600,
    margin=dict(t=50, b=0, l=0, r=0),
    paper_bgcolor='white',
    plot_bgcolor='white'
)

# Update colorbar
fig.update_coloraxes(
    colorbar_title_text="Stunting Rate (%)",
    colorbar_title_side="right",
    colorbar_thickness=15,
    colorbar_len=0.5,
    colorbar_title_font={'size': 12}
)

fig.show()

These maps illustrate the geographic distribution of child stunting across Africa and Asia, revealing distinct regional patterns:

Concentration in Sub-Saharan Africa: Countries in the Sahel region and Central Africa show some of the highest stunting rates, with over 30% of children affected in many nations.
South Asian hotspot: Countries like India, Pakistan, Bangladesh, and Nepal form a significant hotspot of child stunting, with India displaying particularly concerning rates.
Variation within regions: Both continents show significant variation, with some countries making substantial progress while neighboring nations continue to struggle.

The burden of stunting in these two continents is particularly concerning as they collectively account for over 80% of all stunted children worldwide. Targeted interventions in the darkest green areas represent opportunities for significant improvements in global child nutrition outcomes.

Progress in Reducing Child Stunting

This area chart tracks Bangladesh’s progress in reducing child stunting over time. The visualization demonstrates the country’s commitment to improving child nutrition and highlights the effectiveness of targeted interventions implemented over the years. The downward trend represents real improvements in the lives of children and showcases a public health success story that other countries might learn from.

Show code for Bangladesh stunting progress

# Filter Bangladesh stunting data
bangladesh_stunting = combined_indicators[
    (combined_indicators['country'] == 'Bangladesh') &
    (combined_indicators['indicator'].str.contains('stunting|height.for.age', case=False, na=False)) &
    (~combined_indicators['indicator'].str.contains('wasting|weight.for.height', case=False, na=False)) &
    (~combined_indicators['obs_value'].isna()) &
    ((combined_indicators['sex'] == 'Total') | combined_indicators['sex'].isna())
]

# Clean and prepare data
bangladesh_stunting = bangladesh_stunting.copy()
bangladesh_stunting['obs_value'] = bangladesh_stunting['obs_value'].apply(
    lambda x: x/100 if x > 100 else x
)
bangladesh_stunting['obs_value'] = bangladesh_stunting['obs_value'].apply(
    lambda x: 100 if x > 100 else x
)
bangladesh_stunting = bangladesh_stunting.sort_values('time_period')

# Create interactive area chart for Bangladesh stunting trend
bangladesh_data = bangladesh_stunting.sort_values('time_period')
fig = go.Figure()
fig.add_trace(go.Scatter(
    x=bangladesh_data['time_period'],
    y=bangladesh_data['obs_value'],
    fill='tozeroy',
    fillcolor='rgba(0, 186, 56, 0.3)',  # Using rgba format for color with transparency
    line=dict(color=COLORS['green'], width=2),
    name='Stunting Rate',
    hovertemplate='Year: %{x}<br>Stunting Rate: %{y:.1f}%<extra></extra>'
))
fig.update_layout(
    title=dict(
        text='Trend in Child Stunting Rates in Bangladesh (2000-2019)',
        x=0.5,
        font=dict(size=16)
    ),
    xaxis_title='Year',
    yaxis_title='Percentage of Children Stunted',
    plot_bgcolor='white',
    paper_bgcolor='white',
    showlegend=False,
    width=800,
    height=500,
    margin=dict(t=50, b=50, l=50, r=50),
    annotations=[
        dict(
            text='Source: UNICEF Child Indicator Data',
            xref='paper',
            yref='paper',
            x=0.5,
            y=-0.15,
            showarrow=False,
            font=dict(size=10)
        )
    ]
)
fig.update_xaxes(gridcolor=COLORS['grid'], zeroline=False)
fig.update_yaxes(gridcolor=COLORS['grid'], zeroline=False)
fig.show()

This area chart illustrates Bangladesh’s remarkable journey in reducing the prevalence of child stunting over time. The dark green area represents the percentage of children under 5 who are stunted (height-for-age below -2 standard deviations from the median).

The downward slope of the chart tells a positive story of nutrition improvement in Bangladesh, which has been achieved through a combination of:

Targeted nutrition interventions - Including micronutrient supplementation, promotion of exclusive breastfeeding, and complementary feeding practices
Broader development progress - Economic growth, improved food security, and enhanced maternal education
Health system strengthening - Better access to healthcare services, improved water and sanitation facilities, and increased immunization coverage

Bangladesh’s progress serves as an inspiring example for other countries facing similar challenges. The consistent decline demonstrates that with sustained commitment and evidence-based approaches, significant improvements in child nutrition outcomes are achievable even in resource-constrained settings.

Conclusion

The analysis reveals persistent and significant challenges in both child stunting and HIV awareness among youth worldwide. Stunting remains highly concentrated in South Asia and Sub-Saharan Africa, with countries such as India, Bangladesh, and several Central African nations bearing the greatest burden.

Despite some notable progress, represents by Bangladesh’s steady reduction in stunting rates, millions of children continue to face the lifelong consequences of undernutrition. At the same time, global HIV knowledge among youth displays stark disparities, with leading countries in Eastern and Southern Africa achieving much higher awareness rates than those at the bottom of the scale.

Gender gaps further compound these issues, as males generally possess higher HIV knowledge than females in most countries.

These findings emphasised the urgent need for targeted, region-specific interventions that address both the immediate and systemic factors driving these public health challenges.

Solutions

Prioritize targeted investments in countries and regions with the highest rates of child stunting and lowest HIV knowledge, particularly in South Asia and Sub-Saharan Africa.
Develop and implement gender-sensitive education campaigns to close the HIV knowledge gap between males and females, ensuring equitable access to information for all youth.
Integrate nutrition initiatives with broader development strategies, including improving maternal education, healthcare access, water and sanitation, and food security. 4.Establish robust data monitoring systems to track progress, identify emerging gaps, and enable adaptive, evidence-based policymaking.
Foster multisectoral collaboration among governments, NGOs, and international organizations to address both immediate needs and the underlying social determinants of health.
Promote the exchange of best practices by learning from successful countries, adapting proven interventions to local contexts, and scaling up effective solutions.

By acting on these recommendations, we can accelerate progress and help more children grow up healthy and strong.

--- title: "GROWING HOPE" subtitle: "**Understanding Child Growth & HIV Awareness Worldwide**" format: html: toc: true theme: sandstone code-fold: true # Make code foldable code-tools: true # Add code tools (copy, etc.) code-summary: "Show code" # Text shown on the code folding button embed-resources: true # Embed all resources in the HTML file include-in-header: - text: | <script src="https://cdn.plot.ly/plotly-latest.min.js"></script> page-layout: full execute: echo: true # Show code by default (changed from false) warning: false message: false error: false jupyter: python3 author: "Lukman Khiruddin" date: "April 22 2025" --- ------------------------------------------------------------------------ ## [Introduction]{style="font-family: serif"} In every corner of our world, the journey of childhood unfolds with both promise and pitfalls. Where the glow of hope is often shadowed by the realities of stunted growth and the silent spread of HIV. This dashboard invites you to explore the global landscape of child development, weaving together vivid data on child stunting and HIV awareness to reveal the stark contrasts and hidden connections shaping young lives. ```{python} #| code-fold: true #| code-summary: "Show code" # Import necessary libraries import pandas as pd import numpy as np import plotly.express as px import plotly.graph_objects as go from plotly.subplots import make_subplots import geopandas as gpd import json import matplotlib.pyplot as plt # Define color scheme to match R's ggplot2 COLORS = { 'green': '#00BA38', 'red': '#F8766D', 'blue': '#619CFF', 'orange': '#FF9E4A', 'yellow': '#B79F00', 'background': '#F0F0F0', 'grid': '#CCCCCC', 'text': '#000000' } ``` ```{python} #| include: false # Data Exploration unicef_indicator_1 = pd.read_csv("data/unicef_indicator_1.csv") unicef_indicator_2 = pd.read_csv("data/unicef_indicator_2.csv") unicef_metadata = pd.read_csv("data/unicef_metadata.csv") combined_indicators = pd.read_csv("data/Indicator_Combined.csv") ``` ```{python} #| include: false # Check the structure of datasets unicef_indicator_1.head() unicef_indicator_2.head() unicef_metadata.head() ``` ```{python} #| include: false # Checking the shape and missing values in unicef_indicator_1 dataset print(unicef_indicator_1.shape) missing_values_1 = unicef_indicator_1.isna().sum() print(missing_values_1) # In the unicef_indicator_1 dataset, four columns contain only missing values - removing them ``` ```{python} #| include: false # Fill missing values of alpha_2_code with alpha_3_code unicef_indicator_1['alpha_2_code'] = unicef_indicator_1['alpha_2_code'].fillna(unicef_indicator_1['alpha_3_code']) # Drop columns with all missing values from unicef_indicator_1 dataset cols_to_drop = ["time_period_activity_related_to_when_the_data_are_collected", "observation_confidentaility", "observation_status", "unit_multiplier"] unicef_indicator_1 = unicef_indicator_1.drop(columns=cols_to_drop, errors='ignore') ``` ```{python} #| include: false # Missing values in unicef_indicator_2 dataset missing_values_2 = unicef_indicator_2.isna().sum() print(missing_values_2) # Fill missing values of alpha_2_code with alpha_3_code unicef_indicator_2['alpha_2_code'] = unicef_indicator_2['alpha_2_code'].fillna(unicef_indicator_2['alpha_3_code']) ``` ```{python} #| include: false # Missing values in unicef_metadata dataset missing_values_metadata = unicef_metadata.isna().sum() print(missing_values_metadata) # Drop rows with missing values from unicef_metadata dataset unicef_metadata = unicef_metadata.dropna() ``` ## [HIV Knowledge Among Youth by Country]{style="font-family: serif"} This comparison highlights the percentage of young people (aged 15–24) who possess comprehensive, correct knowledge of HIV across countries. By displaying both the leaders and laggards, we can better understand the global disparities in HIV education. This helps policymakers identify both success stories to emulate and areas where urgent action is needed. ::: {.panel-tabset} ## Top 5 Countries ```{python hiv_knowledge_top_tab} #| code-fold: true #| code-summary: "Show code for top countries chart" # Filter HIV knowledge data hiv_knowledge_data = combined_indicators[ combined_indicators['indicator'].str.contains('comprehensive, correct knowledge of HIV', case=False, na=False) ] hiv_knowledge_data = hiv_knowledge_data[~hiv_knowledge_data['obs_value'].isna()] # Get the most recent data for each country and sex recent_hiv_knowledge = hiv_knowledge_data.sort_values('time_period', ascending=False).groupby(['country', 'sex']).first().reset_index() # Calculate average knowledge by country and sort avg_knowledge = recent_hiv_knowledge.groupby('country')['obs_value'].mean().reset_index() avg_knowledge = avg_knowledge.sort_values('obs_value', ascending=True) # Get top 5 countries top_5_countries = avg_knowledge.tail(5) # Create interactive bar chart fig = go.Figure() fig.add_trace(go.Bar( x=top_5_countries['obs_value'], y=top_5_countries['country'], orientation='h', marker_color='darkgreen', text=[f"{x:.1f}%" for x in top_5_countries['obs_value']], textposition='outside', hovertemplate="%{y} Knowledge Rate: %{x:.1f}%<extra></extra>" )) fig.update_layout( title={ 'text': 'Top 5 Countries with Highest HIV Knowledge Youth (15-24 years)', 'x': 0.5, 'xanchor': 'center', 'font': {'size': 16, 'color': 'black'} }, xaxis_title='Percentage (%)', yaxis_title=None, xaxis=dict( range=[0, 75], tickformat='.0f', ticksuffix='%' ), plot_bgcolor='white', paper_bgcolor='white', showlegend=False, height=400, margin=dict(t=80, b=50, l=0, r=50) ) fig.update_xaxes(gridcolor='#cccccc', zeroline=False) fig.update_yaxes(gridcolor='white') fig.show() ``` ## Bottom 5 Countries ```{python hiv_knowledge_bottom_tab} #| code-fold: true #| code-summary: "Show code for bottom countries chart" # Get bottom 5 countries bottom_5_countries = avg_knowledge.head(5) # Create interactive bar chart fig = go.Figure() fig.add_trace(go.Bar( x=bottom_5_countries['obs_value'], y=bottom_5_countries['country'], orientation='h', marker_color='darkgreen', text=[f"{x:.1f}%" for x in bottom_5_countries['obs_value']], textposition='outside', hovertemplate="%{y} Knowledge Rate: %{x:.1f}%<extra></extra>" )) fig.update_layout( title={ 'text': 'Bottom 5 Countries with Lowest HIV Knowledge Youth (15-24 years)', 'x': 0.5, 'xanchor': 'center', 'font': {'size': 16, 'color': 'black'} }, xaxis_title='Percentage (%)', yaxis_title=None, xaxis=dict( range=[0, 75], tickformat='.0f', ticksuffix='%' ), plot_bgcolor='white', paper_bgcolor='white', showlegend=False, height=400, margin=dict(t=80, b=50, l=0, r=50) ) fig.update_xaxes(gridcolor='#cccccc', zeroline=False) fig.update_yaxes(gridcolor='white') fig.show() ``` ::: This tabbed comparison reveals stark disparities in HIV knowledge among youth globally. The top-performing countries (primarily in Eastern and Southern Africa) have achieved knowledge rates above 50%, while the lowest-performing countries show rates below 5%. This significant gap (over 55 percentage points between highest and lowest) highlights implementation disparities in HIV education programs worldwide. Countries at the bottom of the ranking could benefit from adapting successful education strategies from top-performing nations. ## [Top 4 Countries with HIV Knowledge Trends Over Time]{style="font-family: serif"} This line chart tracks the evolution of HIV knowledge among males and females in 4 countries over time. The visual uncovers whether both genders are benefiting equally from education efforts, and whether progress is steady, stagnant, or declining. It provides a clear picture of gender gaps and helps guide future interventions to ensure no one is left behind. ```{python hiv_knowledge_trends} #| code-fold: true #| code-summary: "Show code for trends chart" # Filter data for comprehensive HIV knowledge hiv_knowledge_data = combined_indicators[ combined_indicators['indicator'].str.contains('comprehensive, correct knowledge of HIV', case=False, na=False) ] hiv_knowledge_data = hiv_knowledge_data[~hiv_knowledge_data['obs_value'].isna()] hiv_knowledge_data = hiv_knowledge_data[hiv_knowledge_data['sex'].isin(['Female', 'Male'])] # Count data points by country and sex to find those with the most data country_data_counts = ( hiv_knowledge_data.groupby(['country', 'sex']) .size() .reset_index(name='data_points') .groupby('country') .agg( total_points=('data_points', 'sum'), has_both_sexes=('sex', 'nunique') ) .reset_index() ) # Filter for countries with both sexes and sort by total data points country_data_counts = country_data_counts[country_data_counts['has_both_sexes'] > 1] country_data_counts = country_data_counts.sort_values('total_points', ascending=False) # Get top 4 countries with most data points top_trend_countries = country_data_counts.head(4)['country'].tolist() # Filter data for top countries trend_data = hiv_knowledge_data[hiv_knowledge_data['country'].isin(top_trend_countries)] # Create subplots fig = make_subplots( rows=2, cols=2, subplot_titles=top_trend_countries, vertical_spacing=0.15, horizontal_spacing=0.1 ) # Plot each country colors = {'Female': '#F8766D', 'Male': '#619CFF'} # Match R's default colors for i, country in enumerate(top_trend_countries): row = i // 2 + 1 col = i % 2 + 1 country_data = trend_data[trend_data['country'] == country] for sex in ['Female', 'Male']: sex_data = country_data[country_data['sex'] == sex] fig.add_trace( go.Scatter( x=sex_data['time_period'], y=sex_data['obs_value'], name=sex, line=dict(color=colors[sex], width=2), mode='lines+markers', marker=dict(size=8), showlegend=(i == 0), hovertemplate=f"{sex}: %{{y:.1f}}%<extra></extra>" ), row=row, col=col ) fig.update_layout( title={ 'text': 'HIV Knowledge Trends by Sex & Country Percentage of youth (15-24) with comprehensive knowledge', 'x': 0.5, 'xanchor': 'center', 'font': {'size': 16, 'color': 'black'} }, height=600, showlegend=True, legend=dict( orientation="h", yanchor="bottom", y=-0.2, xanchor="center", x=0.5 ), plot_bgcolor='white', paper_bgcolor='white' ) # Update all axes for i in range(1, 5): fig.update_yaxes( title_text='%', range=[0, 60], tickformat='.0f', ticksuffix='%', gridcolor='#cccccc', zeroline=False, row=(i-1)//2 + 1, col=(i-1)%2 + 1 ) fig.update_xaxes( title_text='Year', gridcolor='#cccccc', zeroline=False, row=(i-1)//2 + 1, col=(i-1)%2 + 1 ) fig.show() ``` This line chart reveals the evolution of HIV knowledge among young people across countries with the richest trend data, showing patterns of change over time for both males and females. ## [Global Gender Gap in HIV Knowledge]{style="font-family: serif"} This scatterplot displays HIV knowledge levels for young people (aged 15-24) across all countries, comparing males and females. Each point represents a country-gender combination, with the most recent available data for each country. The visualization reveals a clear pattern of gender disparity in HIV knowledge globally. ```{python global_hiv_knowledge_scatter} #| code-fold: true #| code-summary: "Show code for global comparison" # Get the most recent data for each country and sex combination hiv_knowledge_global = ( hiv_knowledge_data .sort_values('time_period', ascending=False) .groupby(['country', 'sex']) .first() .reset_index() ) # Filter to just Male and Female data hiv_knowledge_global = hiv_knowledge_global[hiv_knowledge_global['sex'].isin(['Female', 'Male'])] # Create scatter plot with box plots fig = go.Figure() # Add box plots for sex, color in zip(['Female', 'Male'], [COLORS['red'], COLORS['blue']]): sex_data = hiv_knowledge_global[hiv_knowledge_global['sex'] == sex] # Add box plot fig.add_trace(go.Box( x=[sex] * len(sex_data), y=sex_data['obs_value'], name=sex, marker_color=color, boxpoints='all', jitter=0.3, pointpos=-1.8, marker=dict( color=color, size=8, opacity=0.7 ), line=dict(color='black'), fillcolor='white', hovertemplate="Country: %{text} Value: %{y:.1f}%<extra></extra>", text=sex_data['country'] )) # Add horizontal line at median median_value = hiv_knowledge_global['obs_value'].median() fig.add_shape( type='line', x0='Female', x1='Male', y0=median_value, y1=median_value, line=dict( color='darkgray', width=2, dash='dash' ) ) # Update layout fig.update_layout( title={ 'text': 'Global Gender Gap in HIV Knowledge Most recent data on youth (15-24) with comprehensive HIV knowledge by country', 'x': 0.5, 'xanchor': 'center' }, yaxis_title='HIV Knowledge (%)', xaxis_title=None, yaxis=dict(range=[0, 70]), plot_bgcolor='white', height=600, showlegend=False, ) fig.update_yaxes(gridcolor=COLORS['grid'], zeroline=False) fig.show() ``` For comparison, here is the static version of the same visualization: ```{python global_comparison_static} #| code-fold: true #| code-summary: "Show code for static comparison" # Create country comparison data country_comparison = ( hiv_knowledge_global .pivot(index='country', columns='sex', values='obs_value') .reset_index() .dropna() ) # Create figure fig = go.Figure() # Add identity line (y = x) fig.add_trace(go.Scatter( x=[0, 70], y=[0, 70], mode='lines', line=dict(color='gray', dash='dash'), showlegend=False, hoverinfo='skip' )) # Add scatter points fig.add_trace(go.Scatter( x=country_comparison['Female'], y=country_comparison['Male'], mode='markers', marker=dict( color='#3498db', size=10, opacity=0.7 ), text=country_comparison['country'], hovertemplate='%{text} ' + 'Female Knowledge: %{x:.1f}% ' + 'Male Knowledge: %{y:.1f}%<extra></extra>', showlegend=False )) # Add trend line with confidence interval x = country_comparison['Female'].values y = country_comparison['Male'].values z = np.polyfit(x, y, 1) p = np.poly1d(z) x_smooth = np.linspace(min(x), max(x), 100) y_smooth = p(x_smooth) # Calculate confidence intervals residuals = y - p(x) std_dev = np.std(residuals) y_upper = y_smooth + 1.96 * std_dev y_lower = y_smooth - 1.96 * std_dev # Add confidence interval fig.add_trace(go.Scatter( x=x_smooth, y=y_upper, mode='lines', line=dict(width=0), showlegend=False, hoverinfo='skip' )) fig.add_trace(go.Scatter( x=x_smooth, y=y_lower, mode='lines', fill='tonexty', fillcolor='rgba(231, 76, 60, 0.2)', line=dict(width=0), showlegend=False, hoverinfo='skip' )) # Add trend line fig.add_trace(go.Scatter( x=x_smooth, y=y_smooth, mode='lines', line=dict(color='#e74c3c', width=2), showlegend=False, hoverinfo='skip' )) # Add annotations for selected points mask = ((abs(country_comparison['Male'] - country_comparison['Female']) > 10) | (country_comparison['Female'] > 45) | (country_comparison['Male'] > 45)) for _, row in country_comparison[mask].iterrows(): fig.add_annotation( x=row['Female'], y=row['Male'], text=row['country'], showarrow=False, font=dict(size=10), yshift=5 ) # Update layout fig.update_layout( title={ 'text': 'HIV Knowledge: Male vs. Female Comparison Each point represents a country\'s most recent data', 'x': 0.5, 'xanchor': 'center' }, xaxis_title='Female Knowledge (%)', yaxis_title='Male Knowledge (%)', xaxis=dict(range=[0, 70], dtick=10), yaxis=dict(range=[0, 70], dtick=10), plot_bgcolor='white', height=600, showlegend=False, annotations=[ dict( text='Points above dashed line: Male knowledge exceeds female knowledge', xref='paper', yref='paper', x=0.5, y=-0.15, showarrow=False, font=dict(size=10) ) ] ) fig.update_xaxes(gridcolor=COLORS['grid'], zeroline=False) fig.update_yaxes(gridcolor=COLORS['grid'], zeroline=False) fig.show() ``` These visualizations reveal several important patterns in global HIV knowledge: 1. **Persistent Gender Gap**: The boxplots show that, on average, males tend to have higher levels of HIV knowledge than females across countries. The median knowledge level for males is noticeably higher than for females. 2. **Wide Variation Between Countries**: There is substantial variation in HIV knowledge levels across countries, with some countries showing knowledge rates above 50% while others fall below 5%. 3. **Country-Level Gender Disparities**: The comparison plot clearly shows that in most countries (points falling above the dashed line), males have higher knowledge levels than females. However, there are some exceptions where female knowledge exceeds male knowledge. 4. **Positive Correlation**: There is a strong positive correlation between male and female knowledge levels within countries. Countries that do well in educating one gender about HIV tend to also do well with the other gender. 5. **Regional Patterns**: Some of the highest-performing countries (labeled in the upper right) are clustered in Sub-Saharan Africa, where HIV prevalence is higher and education efforts have been more intensive. These findings highlight the need for targeted educational interventions that address gender-specific barriers to HIV knowledge, with particular attention to countries and regions where overall knowledge levels remain low. ## [Child Stunting Burden by Country]{style="font-family: serif"} This map displays the percentage of children under 5 affected by stunting (low height-for-age) in Africa and Asia, the two continents with the highest burden of child malnutrition. By focusing on these regions, we can better visualize where interventions are most urgently needed and identify patterns of child nutrition challenges. ::: {.panel-tabset} ## Africa ```{python stunting_map_africa} #| code-fold: true #| code-summary: "Show code for Africa map" # Load world map data for Africa world = gpd.read_file('https://raw.githubusercontent.com/nvkelso/natural-earth-vector/master/geojson/ne_110m_admin_0_countries.geojson') africa = world[world['CONTINENT'] == 'Africa'].copy() # Filter stunting data - use only Total values for each country stunting_data = combined_indicators[ (combined_indicators['indicator'].str.contains('height.for.age', case=False, na=False) | combined_indicators['indicator'].str.contains('stunting', case=False, na=False)) & (~combined_indicators['obs_value'].isna()) & (combined_indicators['sex'] == 'Total') # Only use Total values ] # Get most recent stunting data for each country recent_stunting = stunting_data.sort_values('time_period', ascending=False).groupby('country').first().reset_index() # Ensure values are in proper percentage range (0-100) recent_stunting['obs_value'] = recent_stunting['obs_value'].apply(lambda x: min(100, max(0, x))) # Create a country name mapping dictionary country_mapping = { "Congo, Democratic Republic of the": "Democratic Republic of the Congo", "Congo, the Democratic Republic of the": "Democratic Republic of the Congo", "Congo": "Republic of the Congo", "Tanzania, United Republic of": "United Republic of Tanzania", "Swaziland": "eSwatini", "Côte d'Ivoire": "Ivory Coast", "Ivory Coast": "Ivory Coast" } # Apply country name mapping recent_stunting['map_country'] = recent_stunting['country'].apply( lambda x: country_mapping.get(x, x) ) # Create choropleth map fig = px.choropleth( recent_stunting, locations='alpha_3_code', color='obs_value', hover_name='country', color_continuous_scale=['lightyellow', 'yellowgreen', 'darkgreen'], range_color=[0, 50], # Set max to 50% as this is a more realistic range for stunting labels={'obs_value': 'Stunting Rate (%)'}, custom_data=['obs_value'] ) # Update hover template to show percentage fig.update_traces( hovertemplate="%{hovertext} Stunting Rate: %{customdata[0]:.1f}%<extra></extra>" ) # Update layout fig.update_layout( title={ 'text': 'Child Stunting Burden in Africa Percentage of children under 5', 'x': 0.5, 'xanchor': 'center', 'font': {'size': 16, 'color': 'black'} }, geo=dict( showframe=False, showcoastlines=True, projection_type='equirectangular', scope='africa', bgcolor='white' ), width=800, height=600, margin=dict(t=50, b=0, l=0, r=0), paper_bgcolor='white', plot_bgcolor='white' ) # Update colorbar fig.update_coloraxes( colorbar_title_text="Stunting Rate (%)", colorbar_title_side="right", colorbar_thickness=15, colorbar_len=0.5, colorbar_title_font={'size': 12} ) fig.show() ``` ## Asia ```{python stunting_map_asia} #| code-fold: true #| code-summary: "Show code for Asia map" # Load world map data for Asia asia = world[world['CONTINENT'] == 'Asia'].copy() # Create a country name mapping dictionary for Asia asia_country_mapping = { "Iran, Islamic Republic of": "Iran", "Korea, Democratic People's Republic of": "North Korea", "Korea, Republic of": "South Korea", "Lao People's Democratic Republic": "Laos", "Syrian Arab Republic": "Syria", "Viet Nam": "Vietnam", "Myanmar": "Burma" } # Apply country name mapping recent_stunting = stunting_data.sort_values('time_period', ascending=False).groupby('country').first().reset_index() recent_stunting['map_country'] = recent_stunting['country'].apply( lambda x: asia_country_mapping.get(x, x) ) # Create choropleth map fig = px.choropleth( recent_stunting, locations='alpha_3_code', color='obs_value', hover_name='country', color_continuous_scale=['lightyellow', 'yellowgreen', 'darkgreen'], range_color=[0, 50], # Set max to 50% as this is a more realistic range for stunting labels={'obs_value': 'Stunting Rate (%)'}, custom_data=['obs_value'] ) # Update hover template to show percentage fig.update_traces( hovertemplate="%{hovertext} Stunting Rate: %{customdata[0]:.1f}%<extra></extra>" ) # Update layout fig.update_layout( title={ 'text': 'Child Stunting Burden in Asia Percentage of children under 5', 'x': 0.5, 'xanchor': 'center', 'font': {'size': 16, 'color': 'black'} }, geo=dict( showframe=False, showcoastlines=True, projection_type='equirectangular', scope='asia', bgcolor='white' ), width=800, height=600, margin=dict(t=50, b=0, l=0, r=0), paper_bgcolor='white', plot_bgcolor='white' ) # Update colorbar fig.update_coloraxes( colorbar_title_text="Stunting Rate (%)", colorbar_title_side="right", colorbar_thickness=15, colorbar_len=0.5, colorbar_title_font={'size': 12} ) fig.show() ``` ::: These maps illustrate the geographic distribution of child stunting across Africa and Asia, revealing distinct regional patterns: 1. **Concentration in Sub-Saharan Africa**: Countries in the Sahel region and Central Africa show some of the highest stunting rates, with over 30% of children affected in many nations. 2. **South Asian hotspot**: Countries like India, Pakistan, Bangladesh, and Nepal form a significant hotspot of child stunting, with India displaying particularly concerning rates. 3. **Variation within regions**: Both continents show significant variation, with some countries making substantial progress while neighboring nations continue to struggle. The burden of stunting in these two continents is particularly concerning as they collectively account for over 80% of all stunted children worldwide. Targeted interventions in the darkest green areas represent opportunities for significant improvements in global child nutrition outcomes. ## [Progress in Reducing Child Stunting]{style="font-family: serif"} This area chart tracks Bangladesh's progress in reducing child stunting over time. The visualization demonstrates the country's commitment to improving child nutrition and highlights the effectiveness of targeted interventions implemented over the years. The downward trend represents real improvements in the lives of children and showcases a public health success story that other countries might learn from. ```{python bangladesh_stunting_progress} #| code-fold: true #| code-summary: "Show code for Bangladesh stunting progress" # Filter Bangladesh stunting data bangladesh_stunting = combined_indicators[ (combined_indicators['country'] == 'Bangladesh') & (combined_indicators['indicator'].str.contains('stunting|height.for.age', case=False, na=False)) & (~combined_indicators['indicator'].str.contains('wasting|weight.for.height', case=False, na=False)) & (~combined_indicators['obs_value'].isna()) & ((combined_indicators['sex'] == 'Total') | combined_indicators['sex'].isna()) ] # Clean and prepare data bangladesh_stunting = bangladesh_stunting.copy() bangladesh_stunting['obs_value'] = bangladesh_stunting['obs_value'].apply( lambda x: x/100 if x > 100 else x ) bangladesh_stunting['obs_value'] = bangladesh_stunting['obs_value'].apply( lambda x: 100 if x > 100 else x ) bangladesh_stunting = bangladesh_stunting.sort_values('time_period') # Create interactive area chart for Bangladesh stunting trend bangladesh_data = bangladesh_stunting.sort_values('time_period') fig = go.Figure() fig.add_trace(go.Scatter( x=bangladesh_data['time_period'], y=bangladesh_data['obs_value'], fill='tozeroy', fillcolor='rgba(0, 186, 56, 0.3)', # Using rgba format for color with transparency line=dict(color=COLORS['green'], width=2), name='Stunting Rate', hovertemplate='Year: %{x} Stunting Rate: %{y:.1f}%<extra></extra>' )) fig.update_layout( title=dict( text='Trend in Child Stunting Rates in Bangladesh (2000-2019)', x=0.5, font=dict(size=16) ), xaxis_title='Year', yaxis_title='Percentage of Children Stunted', plot_bgcolor='white', paper_bgcolor='white', showlegend=False, width=800, height=500, margin=dict(t=50, b=50, l=50, r=50), annotations=[ dict( text='Source: UNICEF Child Indicator Data', xref='paper', yref='paper', x=0.5, y=-0.15, showarrow=False, font=dict(size=10) ) ] ) fig.update_xaxes(gridcolor=COLORS['grid'], zeroline=False) fig.update_yaxes(gridcolor=COLORS['grid'], zeroline=False) fig.show() ``` This area chart illustrates Bangladesh's remarkable journey in reducing the prevalence of child stunting over time. The dark green area represents the percentage of children under 5 who are stunted (height-for-age below -2 standard deviations from the median). The downward slope of the chart tells a positive story of nutrition improvement in Bangladesh, which has been achieved through a combination of: 1. **Targeted nutrition interventions** - Including micronutrient supplementation, promotion of exclusive breastfeeding, and complementary feeding practices 2. **Broader development progress** - Economic growth, improved food security, and enhanced maternal education 3. **Health system strengthening** - Better access to healthcare services, improved water and sanitation facilities, and increased immunization coverage Bangladesh's progress serves as an inspiring example for other countries facing similar challenges. The consistent decline demonstrates that with sustained commitment and evidence-based approaches, significant improvements in child nutrition outcomes are achievable even in resource-constrained settings. ## [Conclusion]{style="font-family: serif"} The analysis **reveals persistent and significant challenges** in both child stunting and HIV awareness among youth worldwide. **Stunting remains highly concentrated in South Asia and Sub-Saharan Africa**, with countries such as India, Bangladesh, and several Central African nations bearing the greatest burden. Despite some notable progress, represents by Bangladesh's steady reduction in stunting rates, **millions of children continue to face the lifelong consequences of undernutrition**. At the same time, global HIV knowledge among youth displays stark disparities, with leading countries in Eastern and Southern Africa achieving much higher awareness rates than those at the bottom of the scale. Gender gaps further compound these issues, as **males generally possess higher HIV knowledge than females** in most countries. These findings emphasised the urgent need for targeted, region-specific interventions that address both the immediate and systemic factors driving these public health challenges. ## [Solutions]{style="font-family: serif"} 1. Prioritize **targeted investments** in countries and regions with the highest rates of child stunting and lowest HIV knowledge, particularly in South Asia and Sub-Saharan Africa. 2. Develop and **implement gender-sensitive education campaigns** to close the HIV knowledge gap between males and females, ensuring equitable access to information for all youth. 3. **Integrate nutrition initiatives** with broader development strategies, including improving maternal education, healthcare access, water and sanitation, and food security. 4.Establish **robust data monitoring systems to track progress**, identify emerging gaps, and enable adaptive, evidence-based policymaking. 5. Foster **multisectoral collaboration** among governments, NGOs, and international organizations to address both immediate needs and the underlying social determinants of health. 6. Promote the **exchange of best practices** by learning from successful countries, adapting proven interventions to local contexts, and scaling up effective solutions. By acting on these recommendations, we can accelerate progress and help more children grow up healthy and strong.